An important challenge in managing deployments of computing resources in a computing system or network is dealing with variable traffic. For instance, in a computing system or network associated with the World Wide Web or Internet, it is important to have sufficient computing resources (e.g., web servers, application servers, transaction/database servers) supporting a web site to ensure that the end-user experience is not compromised (e.g., by slow response time), even when the web site is under heavy load with respect to the utilization of one or more applications executed in association with the web site. As is known, an application generally refers to a one or more computer programs designed to perform one or more specific functions, e.g., supply chain management.
One approach to sizing a deployment supporting a particular application is to estimate the anticipated workload traffic pattern, and use enough resources to accommodate the peak anticipated load, using capacity planning approaches. This static arrangement can result in significant resource under-utilization since most workload traffic is quite variable, e.g., with marked diurnal, weekly, etc., patterns.
A refinement on the above approach is to do scheduled or planned source reallocation based on a long-term (e.g., one to several days) forecast of anticipated traffic. This approach is also often inadequate as it relies on the accuracy of a long-term forecast (which may, e.g., underestimate the success of a sales promotion) and is also exposed to unanticipated events (e.g., a traffic surge at news web sites such as experienced at CNN's web site on Sep. 11, 2001).
Another key disadvantage of existing computing deployment approaches is that they generally require some form of manual intervention, e.g., via expert operators, to adjust for resource imbalance.
Accordingly, it would be desirable to have automated or autonomic techniques for managing a computing deployment, associated with a computing system or network, which handle variable workload more efficiently and effectively than existing approaches.